In this work, we evaluate the Criss-cross Attention Module (CCAM) [1]}. CCAM, which is depicted in Figure REF , recursively integrates global context across feature maps in a fast and clever criss-cross fashion. By integrating this attention mechanism, our proposed model outperforms previous approaches in the state-of-the-art by a slight margin, but it attains real-time performances, which makes our method clinically applicable both in inference time and robustness.
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